Since its inception, biometric sensing technology has revolutionized identification and authentication processes. The ability to capture and store biometric data in a digital file of minimal size has yielded immense benefits in fields such as law enforcement, forensics, and information security.
However, the widespread adoption of biometric sensing technology in a broad range of applications has faced a number of obstacles. When biometric sensing technology is used for authentication (for example, for unlocking a mobile device), the process is inherently noisy or imperfect. For example, if the biometric sensor is a fingerprint sensor, it is possible that another person (i.e., an “imposter”) has a similar enough fingerprint to the fingerprint of the correct user so that the imposter is able to authenticate with his or her own fingerprint. This phenomenon is referred to as a “false acceptance.” The rate at which false acceptance occurs for a given authentication scheme is referred to as the “false acceptance rate” (FAR).
Another problem with using electronic sensing technology for authentication is that sometimes the correct user is not able to authenticate. This may be caused by, for example, a poor quality image used for verification, a dirty or blemished finger, or simply poor placement on the sensor, among other reasons. The phenomenon of not authenticating the correct user is referred to as a “false rejection.” The rate at which false rejection occurs for a given authentication scheme is referred to as the “false rejection rate” (FRR).
Accordingly, there remains a need in the art for a biometric authentication scheme that can minimize the “false rejection rate” (FRR) while maintaining a low “false acceptance rate” (FAR).